Demo: Statistically Significant Results On Biases and Errors of LLMs Do Not Guarantee Generalizable Results
This work addresses the need for robust evaluation methods in medical AI to prevent misleading conclusions from statistically significant but non-generalizable results, particularly for researchers and practitioners using LLMs in high-stakes domains.
The paper tackles the problem of ensuring reliable evaluation of medical chatbots by developing an infrastructure to probe LLMs with realistic queries and assess their answers using multiple LLM-as-a-judge setups, finding low inter-LLM agreement (average Cohen's Kappa κ=0.118) and that only specific LLM pairs yield statistically significant differences across factors like writing styles, genders, and races.
Recent research has shown that hallucinations, omissions, and biases are prevalent in everyday use-cases of LLMs. However, chatbots used in medical contexts must provide consistent advice in situations where non-medical factors are involved, such as when demographic information is present. In order to understand the conditions under which medical chatbots fail to perform as expected, we develop an infrastructure that 1) automatically generates queries to probe LLMs and 2) evaluates answers to these queries using multiple LLM-as-a-judge setups and prompts. For 1), our prompt creation pipeline samples the space of patient demographics, histories, disorders, and writing styles to create realistic questions that we subsequently use to prompt LLMs. In 2), our evaluation pipeline provides hallucination and omission detection using LLM-as-a-judge as well as agentic workflows, in addition to LLM-as-a-judge treatment category detectors. As a baseline study, we perform two case studies on inter-LLM agreement and the impact of varying the answering and evaluation LLMs. We find that LLM annotators exhibit low agreement scores (average Cohen's Kappa $κ=0.118$), and only specific (answering, evaluation) LLM pairs yield statistically significant differences across writing styles, genders, and races. We recommend that studies using LLM evaluation use multiple LLMs as evaluators in order to avoid arriving at statistically significant but non-generalizable results, particularly in the absence of ground-truth data. We also suggest publishing inter-LLM agreement metrics for transparency. Our code and dataset are available here: https://github.com/BBN-E/medic-neurips-2025-demo.